12 Biomedical Engineering Fundamentals
603
10. A.V. Oppenheim, A.S. Willsky, S.H. Nawab, Signals and Systems, 2nd edn. (Prentice Hall,
Upper Saddle River, 1997)
11. S. Mukhopadhyay. NPTEL (2016). [Online]. http://textofvideo.nptel.ac.in/108105088/
lec7.pdf
12. S.R. Devasahayam, Signals and Systems in Biomedical Engineering (Springer US, Boston,
2000)
13. J. Schroeder, Signal processing via Fourier-Bessel series expansion. Digit. Signal Process. 3(2),
112 (1993)
14. S. Gupta, K.H. Krishna, R.B. Pachori, M. Tanveer, Fourier-Bessel series expansion based
technique for automated classification of focal and non-focal EEG signals, in International
Joint Conference on Neural Networks (IJCNN), 2018, pp. 1–6
15. A.S. Hood, R.B. Pachori, V.K. Reddy, P. Sircar, Parametric representation of speech employing
multi-component AFM signal model. Int. J. Speech Technol. 18(3), 287–303 (2015)
16. R.B. Pachori, Discrimination between ictal and seizure-free EEG signals using empirical mode
decomposition. Res. Lett. Signal Process. 14, 2008 (2008)
17. R.B. Pachori, P. Sircar, EEG signal analysis using FB expansion and second-order linear TVAR
process. Signal Process. 88(2), 415–420 (2008)
18. R.B. Pachori, P. Sircar, A new technique to reduce cross terms in the Wigner distribution. Digit.
Signal Process. 17(2), 466–474 (2007)
19. R.B. Pachori, P. Sircar, Analysis of multicomponent AM-FM signals using FB-DESA method.
Digit. Signal Process. 20(1), 42–62 (2010)
20. R.B. Pachori, P. Avinash, K. Shashank, R. Sharma, U.R. Acharya, Application of empirical
mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Syst.
Appl. 42(9), 4567–4581 (2015)
21. S. Sood, M. Kumar, R.B. Pachori, U.R. Acharya, Application of empirical mode
decomposition–based features for analysis of normal and CAD heart rate signals. J. Mech.
Med. Biol. 16(1), 1640002 (2016)
22. P. Jain, R.B. Pachori, Time-order representation based method for epoch detection from speech
signals. J. Intell. Syst. 21(1), 79–95 (2012)
23. R.B. Pachori, M. Kumar, P. Avinash, K. Shashank, U.R. Acharya, An improved online
paradigm for screening of diabetic patients using RR-interval signals. J. Mech. Med. Biol.
16(1), 1640003 (2016)
24. R.B. Pachori, P. Sircar, Non-stationary Signal Analysis: Methods Based on Fourier-Bessel
Representation (LAP LAMBERT Academic Publishing, Germany, 2010)
25. A. Bhattacharyya, L. Singh, R.B. Pachori, Fourier–Bessel series expansion based empirical
wavelet transform for analysis of non-stationary signals. Digit. Signal Process. 78, 185–196
(2018)
26. R.G. Andrzejak et al., Indications of nonlinear deterministic and finite-dimensional structures
in time series of brain electrical activity: Dependence on recording region and brain state. Phys.
Rev. E 64 6, 061907 (2001)
27. M.V. Gils, Lecture 01: Introduction and recapitulation of essential techniques (2015). [Online].
https://mycourses.aalto.fi/course/view.php?id=9367&lang=fi
28. B. Mulgrew, P. Grant, J. Thompson, Digital Signal Processing: Concepts and Applications
(Macmillan Education London, 1999)
29. V. Singh, K. Veer, R. Sharma, S. Kumar, Comparative study of FIR and IIR filters for the
removal of 50 Hz noise from EEG signal. Int. J. Biomed. Eng. Technol. 22(3), 250–257 (2016)
30. K. Najarian, R. Splinter, Biomedical Signal and Image Processing (CRC Press, Boca Raton,
2005)
31. A.L. Goldberger et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new
research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
32. M. Tangermann et al., Review of the BCI competition IV. Front. Neurosci. 6, 55 (2012)
33. R.M. Rangayyan, Biomedical Signal Analysis, vol 33 (Wiley, Hoboken, 2015)
34. E. Kaniusas, Biomedical Signals and Sensors I (Springer, Berlin, Heidelberg, 2012)
35. E. Kaniusas, Biomedical Signals and Sensors II (Springer, Berlin, Heidelberg, 2015)
603
10. A.V. Oppenheim, A.S. Willsky, S.H. Nawab, Signals and Systems, 2nd edn. (Prentice Hall,
Upper Saddle River, 1997)
11. S. Mukhopadhyay. NPTEL (2016). [Online]. http://textofvideo.nptel.ac.in/108105088/
lec7.pdf
12. S.R. Devasahayam, Signals and Systems in Biomedical Engineering (Springer US, Boston,
2000)
13. J. Schroeder, Signal processing via Fourier-Bessel series expansion. Digit. Signal Process. 3(2),
112 (1993)
14. S. Gupta, K.H. Krishna, R.B. Pachori, M. Tanveer, Fourier-Bessel series expansion based
technique for automated classification of focal and non-focal EEG signals, in International
Joint Conference on Neural Networks (IJCNN), 2018, pp. 1–6
15. A.S. Hood, R.B. Pachori, V.K. Reddy, P. Sircar, Parametric representation of speech employing
multi-component AFM signal model. Int. J. Speech Technol. 18(3), 287–303 (2015)
16. R.B. Pachori, Discrimination between ictal and seizure-free EEG signals using empirical mode
decomposition. Res. Lett. Signal Process. 14, 2008 (2008)
17. R.B. Pachori, P. Sircar, EEG signal analysis using FB expansion and second-order linear TVAR
process. Signal Process. 88(2), 415–420 (2008)
18. R.B. Pachori, P. Sircar, A new technique to reduce cross terms in the Wigner distribution. Digit.
Signal Process. 17(2), 466–474 (2007)
19. R.B. Pachori, P. Sircar, Analysis of multicomponent AM-FM signals using FB-DESA method.
Digit. Signal Process. 20(1), 42–62 (2010)
20. R.B. Pachori, P. Avinash, K. Shashank, R. Sharma, U.R. Acharya, Application of empirical
mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Syst.
Appl. 42(9), 4567–4581 (2015)
21. S. Sood, M. Kumar, R.B. Pachori, U.R. Acharya, Application of empirical mode
decomposition–based features for analysis of normal and CAD heart rate signals. J. Mech.
Med. Biol. 16(1), 1640002 (2016)
22. P. Jain, R.B. Pachori, Time-order representation based method for epoch detection from speech
signals. J. Intell. Syst. 21(1), 79–95 (2012)
23. R.B. Pachori, M. Kumar, P. Avinash, K. Shashank, U.R. Acharya, An improved online
paradigm for screening of diabetic patients using RR-interval signals. J. Mech. Med. Biol.
16(1), 1640003 (2016)
24. R.B. Pachori, P. Sircar, Non-stationary Signal Analysis: Methods Based on Fourier-Bessel
Representation (LAP LAMBERT Academic Publishing, Germany, 2010)
25. A. Bhattacharyya, L. Singh, R.B. Pachori, Fourier–Bessel series expansion based empirical
wavelet transform for analysis of non-stationary signals. Digit. Signal Process. 78, 185–196
(2018)
26. R.G. Andrzejak et al., Indications of nonlinear deterministic and finite-dimensional structures
in time series of brain electrical activity: Dependence on recording region and brain state. Phys.
Rev. E 64 6, 061907 (2001)
27. M.V. Gils, Lecture 01: Introduction and recapitulation of essential techniques (2015). [Online].
https://mycourses.aalto.fi/course/view.php?id=9367&lang=fi
28. B. Mulgrew, P. Grant, J. Thompson, Digital Signal Processing: Concepts and Applications
(Macmillan Education London, 1999)
29. V. Singh, K. Veer, R. Sharma, S. Kumar, Comparative study of FIR and IIR filters for the
removal of 50 Hz noise from EEG signal. Int. J. Biomed. Eng. Technol. 22(3), 250–257 (2016)
30. K. Najarian, R. Splinter, Biomedical Signal and Image Processing (CRC Press, Boca Raton,
2005)
31. A.L. Goldberger et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new
research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
32. M. Tangermann et al., Review of the BCI competition IV. Front. Neurosci. 6, 55 (2012)
33. R.M. Rangayyan, Biomedical Signal Analysis, vol 33 (Wiley, Hoboken, 2015)
34. E. Kaniusas, Biomedical Signals and Sensors I (Springer, Berlin, Heidelberg, 2012)
35. E. Kaniusas, Biomedical Signals and Sensors II (Springer, Berlin, Heidelberg, 2015)